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Gaining Insights from Conversations: AI's Impact on Data Analysis in Health Emergencies

This post explores the transformative potential of AI in data analytics, especially during health emergencies.


  • Telephone Poll Simulation: A fictive telephone poll conversation was crafted, simulating an interaction between an interviewer and someone impacted by a cholera outbreak.

  • Text-to-Voice Transformation: The script was converted into an audio file using PlayHT, a text-to-voice platform. While the synthesised voices lacked some emotional nuances, they effectively conveyed the content.

  • Audio Analysis with Looppanel: The audio file was uploaded to Looppanel, an AI platform that analysed the content and extracted key insights from the conversation.

  • The efficiency of AI: Instead of manual analysis, AI quickly highlighted the most salient points, capturing the essence of the interviewee's sentiments and concerns.

  • Implications for Health Emergencies: Because AI offers quick analysis, health organisations can respond quickly during crises.

  • Advantages:

    Avoids overlooking critical information.

    Allows efficient resource allocation.

    Enhances the effectiveness of interventions.

    Fosters trust between health agencies and communities.

In health emergencies, clear and concise information is crucial. Social listening, both online and offline, helps health organisations capture the concerns and needs of communities.

Telephone surveys, an important method for offline social listening, provide direct feedback from individuals and shed light on their understanding, behaviour, and challenges related to health emergencies.

By actively listening through these channels, health organisations can better align their strategies, ensure effective communication, and build trust in the community.

While telephone surveys are undeniably effective in gaining rich first-hand insights, they also come with several challenges. The nature of these surveys often results in a wealth of unstructured information despite the interviewer's adherence to a script.

Respondents may recount detailed anecdotes, express emotions, or digress into related topics in their eagerness to share. This complexity can make the analysis phase daunting.

Sifting through the myriad responses to identify recurring themes, feelings, and actionable insights requires great attention and expertise.

It also requires ensuring that the essence of each respondent's perspective is captured and accurately represented. While telephone surveys are a treasure trove of information, they also underscore the importance of robust data processing and analysis mechanisms.

This is where artificial intelligence (AI) comes in, fundamentally changing the field of data analysis. In the complicated task of deciphering vast amounts of unstructured data from telephone surveys, AI can be a formidable ally.

Traditional methods of data analysis, while reliable, are time-consuming and don't always capture the nuances of large data sets. AI, on the other hand, can quickly process and analyse these data sets, spotting patterns, sentiments, and themes that may be missed by the human eye.

In addition, AI algorithms can be trained to recognise specific keywords, phrases, or sentiments to ensure that even the most subtle insights are not lost in the crowd.

By using AI, health organisations can not only speed up the analytics process but also improve the accuracy and depth of their findings. This, in turn, enables more informed decision-making and a tailored approach to address community concerns and needs in health emergencies.

To understand the potential of AI to improve telephone survey analysis, I experimented. I simulated a telephone survey call, representing the background as a cholera outbreak.

This scenario is not uncommon, and the questions asked during such health emergencies follow a certain standard that aims to capture the impact, knowledge, and behaviours of the affected population.

To create this fictional conversation, I turned to ChatGPT, a modern language model. With its help, and based on my own professional experience in the field, I formulated a series of questions that an interviewer might ask a person affected by the cholera outbreak.

ChatGPT not only helped formulate these questions but also generated potential answers that mimicked the varied and often complicated responses one might expect in a real-life scenario.

ChatGPT generates a conversational telephone script.

The result was a comprehensive and realistic telephone conversation that was rich in detail and depth. This served as the basis for the next phase of testing: the creation of the actual telephone conversation in an audio file format.

I converted the carefully crafted and edited script of the phone conversation into an audio file using PlayHT, a text-to-voice platform. The platform's speech synthesis functions recreated the conversation and aurally reproduced the dialogue between the interviewer and the interviewee.

PlayHT - from text to AI-generated voice/audio

It is important to highlight that, although the audio file produced was clear and distinct, it failed to capture the emotional depth one would anticipate from someone going through a cholera outbreak.

The synthesised voices conveyed the content but lacked some of the human nuances that might be present in a real conversation in such distressing circumstances.

Despite this limitation, the audio served its purpose well. It provided a tangible and audible rendition of the telephone survey and set the stage for what comes next: the complex task of analysing the conversation.

This step would further explore the potential of AI in extracting insights from such rich and multi-layered data.

To continue the experiment, I took the generated audio file to Looppanel, an Artificial Intelligence platform known for its capabilities in analysing video and audio content.

The results that followed were nothing short of remarkable. Looppanel's advanced algorithms dove deep into the audio file and meticulously analysed the conversation to extract important insights.

Instead of manually sifting through the entire conversation, the AI quickly highlighted key points from the interviewee's answers.

These AI-generated notes were succinct but captured the essence of the interviewee's feelings, concerns, and experiences related to the cholera outbreak.

The advantages of this approach are manifold.

Firstly, it ensures that no critical piece of information is overlooked, even in the most extensive of conversations.

It also helps allocate resources more efficiently, allowing human analysts to focus on making strategic decisions instead of getting caught up in data processing details.

But perhaps the most significant advantage comes to the fore in the context of health emergencies. In situations where every second counts, the rapidity and precision offered by AI become invaluable assets.

The ability to swiftly glean actionable insights from telephone surveys means that health organisations can be more agile in their response, tailoring their strategies based on real-time feedback from the ground.

This not only enhances the effectiveness of interventions but also fosters a deeper sense of trust and understanding between health agencies and the communities they serve.

By using Artificial Intelligence platforms like Looppanel, health workers can bypass time-consuming manual analysis to ensure they stay ahead of the needs and concerns of affected communities.

In my exploration of AI's capabilities in data analytics, as demonstrated through the telephone poll experiment, it's crucial to acknowledge the preparatory steps behind the scenes.

AI systems, while powerful, require meticulous training to ensure their accuracy and reliability. The data they analyse, especially when derived from platforms like Looppanel, must be cross-referenced and validated for accuracy.

Yet, even with these necessary checks in place, the efficiency gains are undeniable. The time traditionally spent on manual data analysis can be significantly reduced, allowing health organisations to act more swiftly.

In the context of health emergencies, where every moment counts, these time savings can be the difference between timely interventions and missed opportunities.

While AI doesn't replace the need for human oversight, it undeniably amplifies our capacity to respond effectively and promptly in critical situations.

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